{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM：支持向量机\n",
    "\n",
    "- 与传统算法进行对比，看看SVM究竟能带来什么样的效果\n",
    "\n",
    "- 软间隔的作用，这么复杂的算法肯定会导致过拟合现象，如何来进行解决呢？\n",
    "\n",
    "- 核函数的作用，如果只是做线性分类，好像轮不到SVM登场了，核函数才是它的强大之处！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 支持向量机带来的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programing\\Anaconda3\\envs\\ml_env\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  from collections import Mapping, defaultdict\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris['data'][:, (2, 3)]\n",
    "y = iris['target']\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "svm_clf = SVC(kernel='linear', C=float('inf'))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.29411744 0.82352928]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 5.5, 0.0, 2.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一般的模型\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5 * x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax, sv=True):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "    print(w)\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0] / w[1] * x0 - b / w[1]\n",
    "    margin = 1 / w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "    if sv:\n",
    "        svs = svm_clf.support_vectors_\n",
    "        plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, 'k-', linewidth=2)\n",
    "    plt.plot(x0, gutter_up, 'k--', linewidth=2)\n",
    "    plt.plot(x0, gutter_down, 'k--', linewidth=2)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs')\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'ys')\n",
    "plt.plot(x0, pred_1, 'g--', linewidth=2)\n",
    "plt.plot(x0, pred_2, 'm-', linewidth=2)\n",
    "plt.plot(x0, pred_3, 'r-', linewidth=2)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs')\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], 'ys')\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.axis([0, 5.5, 0, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据标准化的影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/v2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 软间隔\n",
    "\n",
    "-如果不加入软间隔会遇到哪些问题呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/v3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用超参数C控制软间隔程度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('sacle', StandardScaler(copy=True, with_mean=True, with_std=True)), ('model', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris['data'][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris['target'] == 2).astype(np.float64)  # iris-viginica\n",
    "\n",
    "svm_clf = Pipeline([('sacle', StandardScaler()), ('model', LinearSVC(C=1))])\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比不同C值所带来的效果差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('std', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline((('std', scaler), ('linear_svc', svm_clf1)))\n",
    "\n",
    "scaled_svm_clf2 = Pipeline((('std', scaler), ('linear_svc', svm_clf2)))\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86561748 2.24558655]\n",
      "[1.70812906 3.13972552]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x302.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 4.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6, sv=False)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"g^\")\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6, sv=False)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 在右侧，使用较高的C值，分类器会减少误分类，但最终会有较小间隔。\n",
    "- 在左侧，使用较低的C值，间隔要大得多，但很多实例最终会出现在间隔之内。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 非线性支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAESCAYAAAD+LxMRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWo0lEQVR4nO3df4zkd13H8eebOyrQo55YskdT7dlQhIK0sZuI/OpUEaQJ1JQ/BLWKgVyBkBwqJkBADhYTDGipUmyOwJWAkSBiQpEfFWR61rsge97WWu2Vgl45u1uOH3PeXu1tb/btH7N33R/fvdvr7MznOzPPRzLp7vfHfN99z+f7yeu++52ZyEwkSZKkUfe40gVIkiRJdWAwliRJkjAYS5IkSYDBWJIkSQIMxpIkSRIAG89m4/PPPz+3bt3a1QGPHTvGueee29VzDCP7Us2+VLMv1darL/v27ft+Zj51HUoaOM7zvWNfVjpw4ADtdptLL720dCm143ip1ut5/qyC8datW5mcnOyqkGazSaPR6Oo5hpF9qWZfqtmXauvVl4g42H01g8l5vnfsy0qNRoNWq9X1mBtGjpdqvZ7nvZVCkiRJwmAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkDayIeHNETEbE8Yi4Zdm6J0XERyLi+xFxJCJ297KWLVsgovO46qrGqZ+3bOnlUSWNgn7OL2f1BR+SpFp5AHgf8DLgicvW7aQzxz8L+CFweS8LefDBs1suSWvVz/nFYCxJAyozPwcQEePAhSeXR8TPAq8ELszM/11YvK//FUrSYDEYS9Lw+QXgIPCeiLgOmAZ2ZObfVm0cEduAbQBjY2M0m83HcMjGqmse2/MNn9nZWXuxTKvVot1u25cKjpfFGquuWe8eGYwlafhcCDwH+FvgAuAXgb+PiP/IzP9cvnFm7qRz6wXj4+PZaDTWtZj1fr5B1Ww27cUymzdvptVq2ZcKjpe1We8e+eY7SRo+/wc8ArwvM+cy83bg68BLy5YlSfVmMJak4fNv/T7g2NjZLZektern/GIwlqQBFREbI+IJwAZgQ0Q8ISI2AruB+4G3L2zzAjo36X2lV7XMzEBm5/H1rzdP/Twz06sjShoV/ZxfDMaSNLjeSee2ibcBv7Xw8zsz8xHgGuBq4AjwUeC3M/OeUoVK0iDwzXeSNKAycwewY5V1d9N5050kaY28YixJkiRhMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIGVkS8OSImI+J4RNyyaPnzIuIfIuKHEXE4Iv4mIp5WsFRJGggGY0kaXA8A7wM+vmz5TwA7ga3ARcBRYFdfK5OkAbSxdAGSpMcmMz8HEBHjwIWLln9p8XYR8WHg9v5WJ0mDx2AsScPvxcDdq62MiG3ANoCxsTGazWZXB5udne36OYaRfVmp1WrRbrftSwXHS7Ve98VgLElDLCKeC/wRcM1q22TmTjq3XjA+Pp6NRqOrYzabTbp9jmFkX1bavHkzrVbLvlRwvFTrdV+8x1iShlREPB34ErA9M/+pdD2SVHcGY0kaQhFxEfBVYCIzP1m6HkkaBN5KIUkDKiI20pnHNwAbIuIJwAlgDPhH4KbMvLlgiZI0UAzGkjS43gm8e9HvvwW8B0jgYuDdEXFqfWZu6m95kjRYDMaSNKAycwewY5XV7+lfJZI0HLzHWJIkScJgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEm1EBG3RURGxLXLlkdE3LKw7v2l6pOkUWAwlqR6+ENgHnhfRGxYtPyDwO8AH83MtxWpbMBs2QIRKx9btpSuTBoco3oeGYwlqQYy807gk8CzgOsAIuIdwO8DnwHeUK66wfLgg2e3XNJKo3oebSxdgCTplHcCvw7siIhNwB8DXwGuy8z5opVJ0gjwirEk1URmHgI+BFwE/AWwB7g2M+cWbxcRb4+Ib0bE/0bE4Yi4NSKeU6BkSRoqBmNJqpfDi35+XWY+VLFNA/gI8Hzgl4ATwFcj4im9L0+ShpfBWJJqIiJeQ+fNdjMLi7ZXbZeZL8vMXZn575l5F517kp8KvKA/lUrScDIYS1INRMTVwCeAu4HnAvcAr4+IZ65h9yfTmc9/1LsKB8fY2Nktl7TSqJ5HBmNJKiwiXgh8FjgEvDQzDwPvovMG6bV8dvGNwBSwt2dFDpCZGchc+ZiZOfO+kjpG9TwyGEtSQRFxGfAF4AjwK5k5DZCZnwUmgWsi4kWn2f/PgBcCr8rMdh9KlqShZTCWpEIi4ul0Po4tgZdl5reXbfL2hf9+YJX9bwBeA/xSZn6nZ4VK0ojwc4wlqZDMvA9Y9XukMvOrQFSti4gbgVcDjcy8pzcVStJoMRhL0oCJiJvofBLFrwE/ioiT4Xo2M2fLVSZJg60vt1Is/r7tq65qFP2+7Tp+9/f00Wm2T21nZrb8He3TR6e58pYra1OLfamupXRf6nQe1Wl+6aM30fkkiq8B04seb128UURsjYgvRsSPImImIj4cEV4QkaRV9CUY1+n7tutUy0kTuye468hdTNw+Ua6IRbXccf8dtanFvlTXUrovdTqP6lRLv2RmrPLYsWzTjwDfA54GXA5cSSdUS5IqnNWVgwMHDtBoNB7DYZqrrnlsz9eN5qpr+l8LHD/nOP/yvH8hNyQ3f+Nm9t+4n3Pmzul7HYtrmd8wX5ta7Et1LeX70lx1zaif0zXzM8CHM/NhYCYivgw8u3BNklRb/kmtsINbD5IkAEly8KKDXPKtS6zFWmpfiwbCjcCrI6IJ/ATwcjqfj7xERGwDtgGMjY3RbDa7Oujs7GzXzzGM7MtKrVaLdrttXyo4Xqr1ui+RmWveeHx8PCcnJ8/+IJXvqe44i8OvizrVMn10mov//GIePvHwqWVP3PhEvrP9O2zZ1N8bJK3FWs5Gnc6jXtQSEfsyc/yx7V0fEfEs4FPAZcAGOt+s97t5mon/sc7zizWbTa/WV7AvKzUaDVqtFlNTU6VLqR3HS7X16stq87yfY1zQxO4J5nN+ybJ2tovcO2ot1qLhEhGPo/MZyZ8DzgXOp3PV+E9K1iVJddaXYFyn79uuUy17D+1lrj23ZNlce449h/ZYi7XUupY6nUd1qqVmngL8FJ17jI9n5g+AXcDVZcuSpPrqyz3Gi79Xu/SfBur0Hd/7r99/6ufSfVlcS2n2pVqd+lKn86hO80udZOb3I+K/gDdGxAeBTcDvAHeWrUyS6stbKSRpeF0L/CpwGLgPOAH8XtGKJKnG/FQKSRpSmTkFNErXIUmDwivGkiRJEgZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IWe+ABiFjbY9u2lftv20bjqqvWtv+OHSv3f8Ur1n78nTtX7n/FFWvf/9ZbV+5/wQVr33/fvpX7n2b7FX154IHH3vuIlcfet2/t+15wwcr9b7117ftfccXK/XfuXPv+r3jFyv137Oh67K15/wEYe6c9j85y7K14DPDYu6Lqte927C1iMJYkSZIwGEuSJEkARGaueePx8fGcnJzs6oDNZpNGo9HVcwwj+1LNvlSzL9XWqy8RsS8zx7uvaPA4z/eOfVmp0WjQarWYmpoqXUrtOF6q9Xqe94qxJEmShMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTAYCxJkiQBBmNJkiQJMBhL0tCLiEsi4uGI+FTpWkbV9NFptk9tZ2Z2pnQpGgCOl3IMxpI0/G4Cvlm6iFE2sXuCu47cxcTtE6VL0QBwvJRjMJakIRYRrwZawNdK1zKqpo9Os2tqF0mya2qXVwF1Wo6XsjaWLkCS1BsRcR7wXuCXgdedZrttwDaAsbExms1mV8ednZ3t+jmGyQ333sCJ9gkAHmk/whv++g285ZK3FK6qHlqtFu122/GyiOPl9Ho9vxiMJWl4TQAfy8zvRsSqG2XmTmAnwPj4eDYaja4O2mw26fY5hsX00Wlu++fbOJGdoHMiT3Db927j5tfczJZNWwpXV97mzZtptVqOlwWOlzPr9fzirRSSNIQi4nLgJcANpWsZZRO7J5jP+SXL2tn23lFVcryU5xVjSRpODWArcP/C1eJNwIaIuDQzf75gXSNl76G9zLXnliyba8+x59CeQhWpzhwv5RmMJWk47QQ+vej3t9IJym8sUs2I2n/9/lM/e4uJzsTxUp7BWJKGUGY+BDx08veImAUezszD5aqSpHozGEvSCMjMHaVrkKS68813kiRJEgZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJKkkTJ9dJorb7mSmdmZ0qXUin0RGIwlSRopE7snuOP+O5i4faJ0KbViXwQGY0mSRsb00Wl2Te1iPufZNbXLq6ML7ItOMhhLkjQiJnZPMJ/zALSz7dXRBfZFJxmMJUkaASevis615wCYa895dRT7oqUMxpIkjYDFV0VP8uqofdFSBmNJkkbA3kN7T10VPWmuPceeQ3sKVVQP9kWLbSxdgCRJ6r391+8vXUIt2Rct5hVjSZIkCYOxJEmSBBiMJUmSJMBgLEmSJAEGY0kaWhHxlIj4u4g4FhEHI+I3StckLXb8nOPc96L7/Mxg1YbBWJKG103AHDAG/CbwlxHx7LIlSY86uPUgx37ymJ8ZrNrw49okaQhFxLnAq4DnZOYscEdEfB64DnjbavsdOHCARqPR1bFbrRabN2/u6jmGkX1Z6vg5x5n+hWkIuPkbN7P/xv2cM3dO6bJqw/FSrdd9MRhL0nB6BtDOzHsXLbsTuHL5hhGxDdgG8PjHP55Wq9XVgdvtdtfPMYzsy1KHLjt06ud55rn3afdy4Z0XFqyoXhwv1XrdF4OxJA2nTcCRZcuOAE9evmFm7gR2AoyPj+fk5GRXB242m11fdR5G9uVR00enufjPL4YTCws2wEPPeIgv3/RltmzaUrS2unC8VFuvvkRE5XLvMZak4TQLnLds2XnA0QK1SEtM7J5gPueXLGtn23uNVZzBWJKG073Axoi4ZNGyy4C7C9UjnbL30F7m2nNLls2159hzaE+hiqQOb6WQpCGUmcci4nPAeyPi9cDlwDXA88tWJsH+6/cD0Gg0aLVaTE1NFa5I6vCKsSQNrzcBTwS+B/w18MbM9IqxJK3CK8aSNKQy84fAr5WuQ5IGhVeMJUmSJAzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiQAIjPXvnHEYeBgl8c8H/h+l88xjOxLNftSzb5UW6++XJSZT12H5xk4zvM9ZV+q2Zdq9qVaT+f5swrG6yEiJjNzvK8HHQD2pZp9qWZfqtmXevB1qGZfqtmXavalWq/74q0UkiRJEgZjSZIkCSgTjHcWOOYgsC/V7Es1+1LNvtSDr0M1+1LNvlSzL9V62pe+32MsSZIk1ZG3UkiSJEkYjCVJkiTAYCxJkiQBNQjGEXFJRDwcEZ8qXUtpEfFjEfGxiDgYEUcjYn9EvLx0XSVExFMi4u8i4thCP36jdE2lOT7OzPmknnxdHuV5/Cjn+ZUcH2fW6/mkeDAGbgK+WbqImtgIfBe4Evhx4F3AZyJia8GaSrkJmAPGgN8E/jIinl22pOIcH2fmfFJPvi6P8jx+lPP8So6PM+vpfFI0GEfEq4EW8LWSddRFZh7LzB2Z+d+ZOZ+ZXwD+C7iidG39FBHnAq8C3pWZs5l5B/B54LqylZXl+Dg955N68nVZyvO4w3m+muPj9PoxnxQLxhFxHvBe4A9K1VB3ETEGPAO4u3QtffYMoJ2Z9y5adicw6lcSlhjh8bGC80k9+bqc2Qifx87zazDC42OFfs0nJa8YTwAfy8zvFqyhtiLi8cBfAZ/IzHtK19Nnm4Ajy5YdAZ5coJZaGvHxUcX5pJ58XU5jxM9j5/kzGPHxUaUv80lPgnFENCMiV3ncERGXAy8BbujF8evqTH1ZtN3jgE/SuffqzcUKLmcWOG/ZsvOAowVqqR3Hx1KjOp+U5jxfzXl+zZznT8PxsVQ/55ONvXjSzGycbn1EvAXYCtwfEdD5l+OGiLg0M3++FzXVwZn6AhCdhnyMzpsRrs7MR3pdVw3dC2yMiEsy81sLyy7DPyU5Pqo1GMH5pDTn+WrO82vmPL8Kx0elBn2aT4p8JXREPIml/1J8K53/4Tdm5uG+F1QjEXEzcDnwksycLV1PKRHxaSCB19PpxxeB52fmSE+ajo+VnE/qyddldZ7HHc7z1RwfK/VzPunJFeMzycyHgIdO/h4Rs8DDTpZxEXA9cByYWfhXEcD1mflXxQor403Ax4HvAT+gM/hHfbJ0fFRwPqknX5dqnsdLOM8v4/io1s/5pMgVY0mSJKlu6vAFH5IkSVJxBmNJkiQJg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxiosIm6LiIyIa5ctj4i4ZWHd+0vVJ0nqjvO8BonffKeiIuIy4F+BA8DPZWZ7YfmfAr8PfDQztxUsUZLUBed5DRKvGKuozLwT+CTwLOA6gIh4B53J8jPAG8pVJ0nqlvO8BolXjFVcRFwIfAt4EPgg8BfAV4BXZuZcydokSd1znteg8IqxisvMQ8CHgIvoTJZ7gGuXT5YR8eKI+HxE/M/CPWmv7X+1kqSz5TyvQWEwVl0cXvTz6zLzoYptNgH/DmwH/q8vVUmS1ovzvGrPYKziIuI1dP60NrOwaHvVdpn5xcx8R2Z+FpjvV32SpO44z2tQGIxVVERcDXwCuBt4LnAP8PqIeGbRwiRJ68J5XoPEYKxiIuKFwGeBQ8BLM/Mw8C5gI+BnWkrSgHOe16AxGKuIhc+1/AJwBPiVzJwGWPjz2SRwTUS8qGCJkqQuOM9rEBmM1XcR8XQ6H9OTwMsy89vLNnn7wn8/0NfCJEnrwnleg2pj6QI0ejLzPmDLadZ/FYj+VSRJWk/O8xpUBmMNjIjYBDx94dfHAT8dEZcDP8zM+8tVJklaD87zKs1vvtPAiIgG8PWKVZ/IzNf2txpJ0npznldpBmNJkiQJ33wnSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkAP4fCcOTTWQGCKIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y == 0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y == 1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y == 0], X2D[:, 1][y == 0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y == 1], X2D[:, 1][y == 1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一份有点难度的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"bs\")\n",
    "    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=None, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([('poly', PolynomialFeatures(3)),\n",
    "                               ('scaler', StandardScaler()),\n",
    "                               ('svm_clf', LinearSVC(C=10, loss='hinge'))])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly2_model = Pipeline([('poly', PolynomialFeatures(2)),\n",
    "                        ('scaler', StandardScaler()),\n",
    "                        ('svm_clf', LinearSVC(C=10, loss='hinge'))])\n",
    "poly2_model.fit(X, y)\n",
    "\n",
    "plot_predictions(poly2_model, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poly4_model = Pipeline([('poly', PolynomialFeatures(4)),\n",
    "                        ('scaler', StandardScaler()),\n",
    "                        ('svm_clf', LinearSVC(C=10, loss='hinge'))])\n",
    "poly4_model.fit(X, y)\n",
    "\n",
    "plot_predictions(poly4_model, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM中的核技巧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coef0表示偏置项\n",
    "poly11_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                  (\"svm_clf\",\n",
    "                                   SVC(kernel=\"poly\", degree=3, coef0=1, C=5))])\n",
    "\n",
    "poly11_kernel_svm_clf.fit(X, y)\n",
    "\n",
    "poly1100_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                    (\"svm_clf\",\n",
    "                                     SVC(kernel=\"poly\",\n",
    "                                         degree=3,\n",
    "                                         coef0=100,\n",
    "                                         C=5))])\n",
    "\n",
    "poly1100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly11_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly1100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=10, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly101_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                   (\"svm_clf\",\n",
    "                                    SVC(kernel=\"poly\", degree=10, coef0=1,\n",
    "                                        C=5))])\n",
    "\n",
    "poly101_kernel_svm_clf.fit(X, y)\n",
    "\n",
    "poly10100_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                     (\"svm_clf\",\n",
    "                                      SVC(kernel=\"poly\",\n",
    "                                          degree=10,\n",
    "                                          coef0=100,\n",
    "                                          C=5))])\n",
    "\n",
    "poly10100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly101_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly10100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高斯核函数：\n",
    "- 利用相似度来变换特征\n",
    "\n",
    "* 选择一份一维数据集，并在 $x_1 = -2$ 和 $x_1 = 1$ 处为其添加两个高斯函数。\n",
    "* 接下来，让我们将相似度函数定义为 $γ= 0.3$ 的径向基函数（RBF）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/v5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例如：**$x_1 = -1$** ：它位于距第一个地标距离为**1**的地方，距第二个地标距离为 **2**。因此，其新特征是 $x_2 = exp（-0.3×1^2）≈0.74$ 并且 $x_3 = exp（-0.3×2^2）≈0.30$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img/v6.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk == 0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk == 1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(\n",
    "    r'$\\mathbf{x}$',\n",
    "    xy=(X1D[3, 0], 0),\n",
    "    xytext=(-0.5, 0.20),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk == 0], XK[:, 1][yk == 0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk == 1], XK[:, 1][yk == 1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(\n",
    "    r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "    xy=(XK[3, 0], XK[3, 1]),\n",
    "    xytext=(0.65, 0.50),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = 0.1\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk == 0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk == 1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(\n",
    "    r'$\\mathbf{x}$',\n",
    "    xy=(X1D[3, 0], 0),\n",
    "    xytext=(-0.5, 0.20),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk == 0], XK[:, 1][yk == 0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk == 1], XK[:, 1][yk == 1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(\n",
    "    r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "    xy=(XK[3, 0], XK[3, 1]),\n",
    "    xytext=(0.65, 0.50),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma = 0.5\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk == 0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk == 1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(\n",
    "    r'$\\mathbf{x}$',\n",
    "    xy=(X1D[3, 0], 0),\n",
    "    xytext=(-0.5, 0.20),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk == 0], XK[:, 1][yk == 0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk == 1], XK[:, 1][yk == 1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(\n",
    "    r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "    xy=(XK[3, 0], XK[3, 1]),\n",
    "    xytext=(0.65, 0.50),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=18,\n",
    ")\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "理论情况下会得到怎么维特征呢？可以对每一个实例（样本数据点）创建一个地标，此时会将m*n的训练集转换成m*m的训练集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM中利用了核函数的计算技巧，大大降低了计算复杂度："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 增加 gamma γ 使高斯曲线变窄，因此每个实例的影响范围都较小：决策边界最终变得更不规则，在个别实例周围摆动。\n",
    "- 减少 gamma γ 使高斯曲线变宽，因此实例具有更大的影响范围，并且决策边界更加平滑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('model', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([('scale', StandardScaler()),\n",
    "                               ('model', SVC(kernel='rbf', gamma=5, C=0.001))])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                   (\"svm_clf\",\n",
    "                                    SVC(kernel=\"rbf\", gamma=gamma, C=C))])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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